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How to use AI to analyze responses from citizen survey about diversity and inclusion

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Adam Sabla

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Aug 22, 2025

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This article will give you tips on how to analyze responses from a citizen survey about diversity and inclusion using AI survey response analysis and effective survey analysis tools.

Choosing the right tools for analysis

The right approach depends on the kind of response data you have. For quantitative data—such as how many citizens support a policy or rate inclusivity at their workplace—simple counts and percentages are easy to run in tools like Excel or Google Sheets. You basically tally up the numbers and use built-in functions and charts to see trends.

  • Quantitative data: These are easy to analyze with spreadsheets. For example, if 63% of citizens report seeing improvement in community diversity initiatives, tallying that in Sheets or Excel works well. [1]

  • Qualitative data: Open-ended questions or follow-up responses offer much deeper insights, but there’s no way you’ll read and code all those responses by hand if you get even a few dozen. AI tools let you quickly summarize and visualize what people actually said and why, without drowning in text.

There are two approaches for tooling when dealing with qualitative responses:

ChatGPT or similar GPT tool for AI analysis

Copy-paste and chat: Export your open-ended citizen survey data, then paste it into ChatGPT. You can ask for summaries, run sentiment analysis, or brainstorm follow-up questions with the AI in real time.

Less convenient for scale: This method works for smaller datasets, but when you have hundreds or thousands of responses, managing the data becomes tedious fast. Messaging with AI doesn’t give you much structure (no filters, no tracking which part of your data you’re working with), and you often have to chunk responses and keep track of where you left off.

All-in-one tool like Specific

Purpose-built for survey analysis: With Specific’s AI survey response analysis, AI takes care of both collecting and analyzing all responses, so you never have to wonder where to start nor export and copy data around.

Automatic follow-ups for richer data: Specific’s surveys ask personalized follow-up questions, so you always get more complete, actionable input. You don’t need to plan for every possible answer—AI explores what matters with each respondent, boosting response quality across citizen diversity and inclusion projects. Read more on the automatic AI follow-up questions feature.

Instant summarization & insight generation: AI organizes open-text responses, pinpoints patterns, and distills the essence of what citizens actually care about—all in a few clicks. No more manual coding or second-guessing your data. You can even chat with the AI about the results, like with ChatGPT, but with collaboration and data management features built in.

Flexible, collaborative workflows: All-in-one tools let you segment, filter, and revisit data easily. You’re always in control of what insights you generate and how you share them with others on your team or in your community. Learn more in this guide to how to create a citizen survey about diversity and inclusion and analyze the results from start to finish.

Useful prompts that you can use for analyzing citizen diversity and inclusion survey data

I find that using the right prompts makes survey response analysis so much easier and more consistent. Strong prompts help the AI focus on what matters most, and you’ll see the same prompts used by survey pros and platforms like Specific.

Prompt for core ideas – This one works brilliantly for rapidly extracting themes from lots of open answers. Here’s the exact prompt:

Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.

Output requirements:

- Avoid unnecessary details

- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top

- no suggestions

- no indications

Example output:

1. **Core idea text:** explainer text

2. **Core idea text:** explainer text

3. **Core idea text:** explainer text

You’ll get a clear, compact list of what’s top of mind for your citizens around diversity and inclusion.

AI always works better with context. Before asking the AI to analyze, describe your survey, situation, and what you’re hoping to find out. For example:

Analyze the survey responses from citizens regarding diversity and inclusion initiatives in urban communities. Focus on identifying key themes and sentiments expressed by the respondents.

Dive deeper into themes: Once you identify a “core idea,” you can ask: Tell me more about [core idea], or even filter for all related answers using the platform’s search.

Prompt for specific topic: Sometimes you just want to know, “Did anyone talk about community engagement?” Use:

Did anyone talk about community engagement in diversity programs? Include quotes.

Other solid prompts for citizen diversity survey analysis:

Prompt for personas – Understand your community’s different voices by asking:

Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.

Prompt for pain points and challenges – Great for surfacing specific inclusion issues:

Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.

Prompt for motivations & drivers – Find out what’s pushing your citizens toward or away from diversity engagement:

From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.

Prompt for sentiment analysis – Get a sense for collective mood and direction:

Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.

Prompt for suggestions & ideas – Let citizens guide your next steps with this one:

Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.

Prompt for unmet needs & opportunities – Uncover gaps by asking:

Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.

If you’d like more ideas on structuring and wording your questions, check out our guide to the best questions for citizen surveys about diversity and inclusion.

How Specific analyzes qualitative data by question type

Open-ended questions (with or without follow-ups): Get a single summary that covers all responses to both the main and any follow-up questions. This means the AI will tie together everyone’s explanations and stories—giving you a richer, more coherent picture.

Choices with follow-ups: For each multiple choice answer, see a separate breakdown of all related follow-up responses. For instance, if someone selects “Disagree” and explains why, you’ll see a curated summary of those “why” reasons specific to that group.

NPS (Net Promoter Score): Each group—detractors, passives, and promoters—gets its own summary, so you’ll know exactly why certain citizens feel the way they do, with insights filtered by each type.

You can pull off similar analysis in ChatGPT, but it’s more manual: you’ll need to sort and summarize responses per type yourself, instead of having it organized automatically. Read more on AI-powered survey response analysis in Specific.

How to manage AI context limits when analyzing large survey datasets

Most AI models—including ChatGPT and those used by survey tools—have a context size limit. That means if you have too many citizen survey responses, you can’t send everything at once for analysis.

Filtering by specific respondents or questions: In Specific, you can set filters so only conversations where people replied to questions you care about will be analyzed—cutting down the volume and helping you stay focused.

Cropping down to focus questions: Another option is cropping—send just the selected questions or topics to the AI. This lets you analyze all responses to a single, high-value question rather than overwhelming the model.

Together, these methods keep analysis sharp and relevant, even for big or complex diversity and inclusion surveys. Find out more in our guide to AI survey analysis.

Collaborative features for analyzing citizen survey responses

Collaboration can be messy when several people want to analyze survey responses—especially for citizen diversity and inclusion projects where input from experts, policymakers, or activists is valuable. You don’t want analysis stuck in one person’s inbox or lost in exported files.

Chat-based AI collaboration: With Specific, I just start a new chat (or several), apply filters, and each chat tracks exactly who started it, what filters are in play, and which segment of data it covers.

Team clarity on every message: Every message in the analysis chat shows who contributed—so if you’re working with other city staff or diversity committee members, it’s clear where each idea or query came from. Avatars and names are linked to each comment for easy follow-up.

Everyone can try their own angles: You and your collaborators can run different analyses at the same time—no waiting for a summary to come out or juggling files. If someone digs into a specific citizen group or theme, that’s in its own, clearly labeled chat. Learn more about best practices in our guide to editing and collaborating on AI surveys.

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Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.